The present disclosure relates generally to a three-dimensional (3-D) vessel tree surface reconstruction method, particularly to a 3-D coronary artery tree surface reconstruction method, and more particularly to a 3-D coronary artery tree surface reconstruction method from a limited number of two-dimensional (2-D) rotational X-ray angiography images.
In percutaneous coronary intervention (PCI) procedures, physicians evaluate and identify coronary artery lesion (stenosis), and prepare catheterization utilizing X-Ray coronary angiographic images. These images are 2-D projection images of a complex coronary artery tree acquired by X-Ray machines called C-Arms either from bi-plane or from mono-plane. 2-D projections cause vessel occlusion, crossing, and foreshortening. To better understand vessel geometry, usually multiple views with different angles are acquired. In addition, 2-D projection image based quantitative coronary analysis (QCA) is usually chosen to determine lesion length and stent size during PCI. However, there are two major limitations of 2-D QCA: foreshortening and out-of-plane magnification errors.
Known in the art there are two major categories of 3-D reconstruction of coronary arteries from angiographic images: tomographic reconstruction, and symbolic surface reconstruction. In some approaches of performing 3-D tomographic reconstruction of coronary arteries, motion artifact was minimized through a pre-computed 4-D motion field. The 4-D motion field is computed from 3-D coronary artery centerline reconstruction and a 4-D parametric motion model fitting. However, tomographic based approach is computation expensive. In addition, it needs sufficient view angles and a limited number of images can cause a blur and low resolution reconstruction. 3-D symbolic reconstruction has been investigated using two projections and multiple projections. The mainstream idea among existing approaches is to reconstruct a 3-D vessel skeleton and then either fit an elliptical model representing a vessel cross-section using 2-D measurement (e.g. segmentation), or estimating vessel radii from 2-D measurement. The focus here is on developing different centerline reconstruction approaches but not surface reconstruction. Elliptical or circular models are symbolic and are not accurate due to lumen deformation and lesion. Siemens Healthcare has developed software offering 3-D symbolic vessel surface visualization and 3-D QCA of lesions and segment anatomy. However, this software is limited by 2 views, and semi-automated 2-D lumen segmentation has to be performed. 2-D vessel lumen segmentation on the projection image is a challenging task due to vessel occlusion and crossing. Therefore, many iterations of user intervention are needed to get a good segmentation for further symbolic surface reconstruction.
While existing vessel reconstruction methods usable in PCI procedures may be suitable for their intended purpose, the art of PCI procedures would be advanced with a method to automatically generate 3-D vessel geometry and perform 3-D QCA in PCI procedures. 3-D vessel geometry including lumen could avoid the limitations from 2-D projection images. 3-D QCA would therefore allow quantitative determination of vessel lumen, grade of stenosis, and virtual fractional flow reserve (FFR).
This background information is provided to reveal information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.